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Commit 66fcb392 authored by Fahad Khalid's avatar Fahad Khalid
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Updated 3rd party license information.

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Tensorflow Tensorflow
Copyright 2016 The TensorFlow Authors. All rights reserved. Copyright 2019 The TensorFlow Authors. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License. you may not use this file except in compliance with the License.
...@@ -34,38 +34,16 @@ limitations under the License. ...@@ -34,38 +34,16 @@ limitations under the License.
Keras Keras
All contributions by François Chollet: Copyright 2015 The TensorFlow Authors. All rights reserved.
Copyright (c) 2015 - 2019, François Chollet.
All rights reserved.
All contributions by Google: Licensed under the Apache License, Version 2.0 (the "License");
Copyright (c) 2015 - 2019, Google, Inc. you may not use this file except in compliance with the License.
All rights reserved. You may obtain a copy of the License at
All contributions by Microsoft:
Copyright (c) 2017 - 2019, Microsoft, Inc.
All rights reserved.
All other contributions:
Copyright (c) 2015 - 2019, the respective contributors.
All rights reserved.
Licensed under The MIT License (MIT)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all http://www.apache.org/licenses/LICENSE-2.0
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR Unless required by applicable law or agreed to in writing, software
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE \ No newline at end of file
SOFTWARE.
# Introduction # Introduction
This example distributes the partitioned MNIST data across multiple ranks This example distributes the partitioned MNIST data across multiple ranks
for truly data distributed training of a shallow ANN for handwritten digit for truly data distributed training of a shallow Artificial Neural Network for
classification. handwritten digit classification.
The Horovod framework is used for seamless distributed training. However, The Horovod framework is used for seamless distributed training. However,
instead of distributing epochs, this example distributes data amongst the instead of distributing epochs, this example distributes data amongst the
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